Research Status, Challenges and Future Prospects of Bearing Fault Diagnosis

DOI

10.2991/978-94-6239-652-4_11How to use a DOI?

Keywords

Bearing fault diagnosis; Deep learning; Multi-source information fusion; Predictive maintenance; Intelligent diagnosis

Abstract

As the core component of rotating machinery, bearing condition monitoring and fault diagnosis are very important to ensure the safety of equipment. This paper systematically reviews the development of bearing fault diagnosis technology, covering three major methods based on signal processing, machine learning and deep learning. Firstly, the typical fault mechanism of bearing is expounded. Secondly, the principles, representative technologies, advantages and disadvantages of the three mainstream diagnostic methods are analyzed in detail. It is pointed out that the signal processing method relies on expert experience, the machine learning method is limited by artificial features, and the deep learning method has data dependence. On this basis, the limitations of current research in variable condition generalization, small sample adaptability and multi-physics fusion are discussed in depth. Finally, the future development trend is prospected, and it is pointed out that the research will focus on multi-source heterogeneous data deep fusion, interpretable and small sample intelligent algorithm innovation, cross-domain diagnosis and edge real-time computing, and finally evolve to the closed-loop intelligent operation and maintenance mode of predictive maintenance and equipment life cycle management to cope with increasingly complex engineering challenges.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Song Qi
AU  - Guoqing Zhu
PY  - 2026
DA  - 2026/04/19
TI  - Research Status, Challenges and Future Prospects of Bearing Fault Diagnosis
BT  - Proceedings of the  2026 5th International Conference on Engineering Management and Information Science (EMIS 2026)
PB  - Atlantis Press
SP  - 101
EP  - 108
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-652-4_11
DO  - 10.2991/978-94-6239-652-4_11
ID  - Qi2026
ER  -